What's a university worth? Changes in the lifestyle and status of post-2000 European Graduates.

The paper is structured in two main chapters, the first presenting a literature review on lifestyle, underlining the main themes approached in recent scientific papers, and conducting factorial analysis as to discriminate the most relevant research directions, and the second dedicated to studying, on the data provided by the European Social Survey, the lifestyle patterns of post-2000 European graduates. The methodological perspective included probit regression and log-linear models, as well as cluster analysis. The main results refer to testing the concept of lifestyle calibration, that we proposed in the paper, on the selected population of young European graduates. A total of four groups, two exhibiting a good lifestyle calibration, and the other two a poor lifesyle calibration, were obtained. Each family of two groups constitutes a lifestyle type, which is characterized in the paper according to values-behaviours coordination, time allocation and its relation to life satisfaction, defined as an estimator of lifestyle calibration. The conclusions include discussions on the inclusion and exclusion of the European graduates population from these groups, which resulted from our analysis.lifestyle ; university graduates ; European society ; values ; behaviours

Clustering of longitudinal viral loads in the Western Cape

Introduction: Routine viral load (VL) monitoring is important for assessing the effectiveness of ART in South Africa. There is little information however, on how the longitudinal VL patterns change for subgroups of persons living with HIV (PLHIV) who have experienced at least one elevated VL. We investigated the possible longitudinal VL patterns that may exist among this unique population. Methods: This mini-dissertation offers three components; a research protocol (Section A), a literature review (Section B) and a journal ready manuscript (Section C). We examined HIV VL data for the Western Cape from 2008 to 2018, taken from the National Health Laboratory Services (NHLS). Using< 1000 copies/mL as a threshold for viral suppression, we identified 109092 individuals who had at least one instance of an elevated VL. A nonparametric (KML-Shape) and a model-based (LCMM) clustering technique were used to identify latent subgroups of longitudinal VL trajectories among these individuals. Results: Both the KML-Shape and LCMM clustering techniques identified five latent viral load trajectory subgroups. KML-Shape found majority of individuals' trajectories belonged to clusters that had a decreasing longitudinal VL trend (76.6% of individuals), while LCMM found a smaller proportion of individuals' trajectories belonged to clusters that had a decreasing longitudinal trend (52.5% of individuals). Most of the trajectory subgroups identified had long periods of low-level viremia. Conclusion: Although majority of individuals belonged to clusters that had downward trends, further research is needed to better understand factors contributing to membership of clusters that did not have a downward longitudinal trend. Understanding these factors may help in the development of targeted HIV prevention programs for these individuals

Randomised controlled trials of complex interventions and large-scale transformation of services

Complex interventions and large-scale transformations of services are necessary to meet the health-care challenges of the 21st century. However, the evaluation of these types of interventions is challenging and requires methodological development. Innovations such as cluster randomised controlled trials, stepped-wedge designs, and non-randomised evaluations provide options to meet the needs of decision-makers. Adoption of theory and logic models can help clarify causal assumptions, and process evaluation can assist in understanding delivery in context. Issues of implementation must also be considered throughout intervention design and evaluation to ensure that results can be scaled for population benefit. Relevance requires evaluations conducted under real-world conditions, which in turn requires a pragmatic attitude to design. The increasing complexity of interventions and evaluations threatens the ability of researchers to meet the needs of decision-makers for rapid results. Improvements in efficiency are thus crucial, with electronic health records offering significant potential

Development of a Composite Health Index in Children with Cystic Fibrosis: A Pipeline for Data Processing, Machine Learning, and Model Implementation using Electronic Health Records

Cystic Fibrosis (CF) is a heterogeneous multi-faceted genetic condition that primarily affects the lungs and digestive system. For children and young people living with CF, timely management is necessary to prevent the establishment of severe disease. Modern data capture through electronic health records (EHR) have created an opportunity to use machine learning algorithms to classify subgroups of disease to understand health status and prognosis. The overall aim of this thesis was to develop a composite health index in children with CF. An iterative approach to unsupervised cluster analysis was developed to identify homogeneous clusters of children with CF in a pre-existing encounter-based CF database from Toronto Canada. An external validation of the model was carried out in a historical CF dataset from Great Ormond Street Hospital (GOSH) in London UK. The clusters were also re-created and validated using EHR data from GOSH when it first became accessible in 2021. The interpretability and sensitivity of the GOSH EHR model was explored. Lastly, a scoping review was carried out to investigate common barriers to implementation of prognostic machine learning algorithms in paediatric respiratory care. A cluster model was identified that detailed four clusters associated with time to future hospitalisation, pulmonary exacerbation, and lung function. The clusters were also associated with different disease related variables such as comorbidities, anthropometrics, microbiology infections, and treatment history. An app was developed to display individualised cluster assignment, which will be a useful way to interpret the cluster model clinically. The review of prognostic machine learning algorithms identified a lack of reproducibility and validations as the major limitation to model reporting that impair clinical translation. EHR systems facilitate point-of-care access of individualised data and integrated machine learning models. However, there is a gap in translation to clinical implementation of machine learning models. With appropriate regulatory frameworks the health index developed for children with CF could be implemented in CF care

A review of data analysis for early-childhood period: taxonomy, motivations, challenges, recommendation, and methodological aspects

Early childhood is a significant period when transitions take place in children. This period is a hot topic among researchers who pursue this domain across different scientific disciplines. Many studies addressed social, scientific, medical, and technical topics during early childhood. Researchers also utilized different analysis measures to conduct experiments on the different types of data related to the early childhood to produce research articles. This paper aims to review and analyze the literature related to early childhood in addition to the data analyses and the types of data used. The factors that were considered to boost the understanding of contextual aspects in the published studies related to early childhood were considered as open challenges, motivations, and recommendations of researchers who aimed to advance the study in this area of science. We systematically searched articles on topics related to early childhood, the data analysis approaches used, and the types of data applied. The search was conducted on five major databases, namely, ScienceDirect, Scopus, Web of Science, IEEE Xplore, and PubMed from 2013 to September 2017. These indices were considered sufficiently extensive and reliable to cover our field of the literature. Articles were selected on the basis of our inclusion and exclusion criteria (n = 233). The first portion of studies (n = 103/233) focused on the different aspects related to the development of children in early age. They discussed different topics, such as the body growth development of children, psychology, skills, and other related topics that overlap between two or more of the previous topics or do not fall into any of the categories but are still under development. The second portion of studies (n = 107/233) focused on different aspects associated with health in early childhood. A number of topics were discussed in this regard, such as those related to family health, medical procedures, interventions, and risk that address the health-related aspects, in addition to other related topics that overlap between two or more of the previous topics or do not fall into any of the categories but are still under health. The remaining studies (n = 23/233) were categorized to the other main category because they overlap between the previous two major categories, namely, development and health, or they do not fall into any of the previous main categories. Early childhood is a sensitive period in every child’s life. This period was studied using different means of data analysis and with the aid of different data types to produce different findings from the previous studies. Research areas on early childhood vary, but they are equally significant. This paper emphasizes the current standpoint and opportunities for research in this area and boosts additional efforts toward the understanding of this research field

Couple and family therapies for post-traumatic stress disorder (PTSD)

This is a protocol for a Cochrane Review (Intervention). The objectives are as follows: The objectives of this review will be to: assess the efficacy of couple and family therapies for adult PTSD, relative to 'no treatment' conditions, 'standard care', and structured or non‐specific individual psychological therapies; examine the clinical characteristics of studies that influence the relative efficacy of these therapies; and critically evaluate methodological features of studies that bias research findings

A study assessing the characteristics of big data environments that predict high research impact: application of qualitative and quantitative methods

BACKGROUND: Big data offers new opportunities to enhance healthcare practice. While researchers have shown increasing interest to use them, little is known about what drives research impact. We explored predictors of research impact, across three major sources of healthcare big data derived from the government and the private sector. METHODS: This study was based on a mixed methods approach. Using quantitative analysis, we first clustered peer-reviewed original research that used data from government sources derived through the Veterans Health Administration (VHA), and private sources of data from IBM MarketScan and Optum, using social network analysis. We analyzed a battery of research impact measures as a function of the data sources. Other main predictors were topic clusters and authors’ social influence. Additionally, we conducted key informant interviews (KII) with a purposive sample of high impact researchers who have knowledge of the data. We then compiled findings of KIIs into two case studies to provide a rich understanding of drivers of research impact. RESULTS: Analysis of 1,907 peer-reviewed publications using VHA, IBM MarketScan and Optum found that the overall research enterprise was highly dynamic and growing over time. With less than 4 years of observation, research productivity, use of machine learning (ML), natural language processing (NLP), and the Journal Impact Factor showed substantial growth. Studies that used ML and NLP, however, showed limited visibility. After adjustments, VHA studies had generally higher impact (10% and 27% higher annualized Google citation rates) compared to MarketScan and Optum (p<0.001 for both). Analysis of co-authorship networks showed that no single social actor, either a community of scientists or institutions, was dominating. Other key opportunities to achieve high impact based on KIIs include methodological innovations, under-studied populations and predictive modeling based on rich clinical data. CONCLUSIONS: Big data for purposes of research analytics has grown within the three data sources studied between 2013 and 2016. Despite important challenges, the research community is reacting favorably to the opportunities offered both by big data and advanced analytic methods. Big data may be a logical and cost-efficient choice to emulate research initiatives where RCTs are not possible